Journal of Computer and Education Research (ISSN:2148-2896)

April 2017 Volume 5 Issue 9

www.joucer.com http://dergipark.gov.tr/jcer

Research Article

Investigating of Preservice Science Teachers’ Self-Confidence Level

about Technological Pedagogical Content Knowledge

Yurdagül BOĞAR1

1 Middle East Technical University, Faculty of Education, Department of Mathematics and Science Education, ybogar@metu.edu.tr

Article Info Abstract

The purpose of this study is to investigate preservice science teachers’

self-confidence level about technological pedagogical content

knowledge with the Technological Pedagogical Content Knowledge

Confidence Survey. In order to determine preservice science teachers’

self-confidence of Technological Pedagogical Content Knowledge

(TPACK) both quantitative and qualitative research methods will be

used in this study. This research will be based on mixed methods

research design. MANOVA was conducted to investigate the impact of

different grade level on perceived technological pedagogical content

knowledge (TPCK) on preservice science teachers. For analysis of

qualitative data, it was conducted the content analysis as the data was

coded, themes were found, the data was organized and defined

according to the codes and themes, and interpretations were made. The

study showed that preservice science teachers have sufficient related to

the four TPACK constructs.

Received:

December 14, 2016

Accepted: March 11, 2017

Online: May 13, 2017

Keywords: Preservice Science

Teacher, Technological Pedagogical

Content Knowledge, Self-Confidence

Introduction

One of the aims of the technology education is developing a technology concept in

students’ minds. In technology education, technological knowledge should be taught with

the normative components of that knowledge, including the ethical norms, in order to make

students justice the nature of technological knowledge. To do this, students should learn

both the functioning of the technological artifacts and norms, standards, and rules of thumbs

of technological knowledge (de Vries, 2005). Moreover, technology makes students more

active and engaged in lessons and stimulates teamwork (Matray & Proulx, 1995). Becta

(2002) report the advantages of using technology in education as greater motivation,

increased self-esteem and confidence, enhanced questioning skills, promoting initiative and

independent learning, improving presentation, developing problem solving capabilities,

To cite this article:

Boğar, Y. (2017). Investigating of preservice science teachers’ self-

confidence level about technological pedagogical content knowledge.

Journal of Computer and Education Research, 5 (9), 125-140.

https://doi.org/10.18009/jcer.54602

126

promoting better information handling skills, increasing ‘time on task’, improving social and

communication skills.

Science Education and Technology

Science teachers are early-adaptors of technology with the use of hand-held graphic

calculators. They started to use technology in science lessons because it makes possible the

lab activities, which cannot be held due to lack of time or equipment (Matray & Proulx,

1995). Technology helps science teachers in terms of solving the environmental regulations,

safety and cleaning up problems. Technology also makes easier the data collection,

experimentation and communication processes with appropriate software programs that

yield immediate graphics or animations. Moreover, it can be more concentrated on process

of science rather than scientific facts with these programs (Savas & Yilmaz-Tuzun, 2012).

The study of Tala (2008) reveals the needs for unification of science and technology

education, although they are considered as separated domains traditionally. Therefore, the

author suggests a new unifying view, techno science in education to increase the coherence

of learning processes of the two elements.

Pedagogical Content Knowledge

Shulman (1986) state the importance of content knowledge of teachers. The author

demonstrated that the content knowledge is the core of the teaching. Then, again Shulman

(1986) first introduced the notion of pedagogical content knowledge (PCK). Shulman (1986)

defines the pedagogical content knowledge as knowing the reasons of difficulty or easiness

of a specific subject matter by knowing different-aged students’ cognitive levels and

backgrounds.

Grossman (1990) elaborated Shulman’s framework in four general areas which are:

(a) subject matter knowledge, (b) general pedagogical knowledge, (c) knowledge of context,

and (d) pedagogical content knowledge.

After little modifications Magnusson et al., (1999) defined the PCK for science

teaching with five components which are (a) orientation toward science teaching, (b)

knowledge of science curriculum, (c) knowledge of assessment for science, (d) knowledge of

science instructional strategies, and (e) knowledge of student science understanding.

Technological Pedagogical Content Knowledge

Mishra and Koehler (2006) recently introduced the union of three different types of

knowledge as representative of what teachers need to know, coining the combined

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framework, “technological pedagogical content knowledge” or “TPACK” (see Figure 1);

however, other researchers have previously included and named technological knowledge as

a component of teacher knowledge (e.g., Hughes, 2005; Niess, 2005) while work prior to

Mishra and Koehler (2006) also formally introduced the concept of TPACK (e.g., Pierson,

2001). The TPACK framework strives to “capture some of the essential qualities of

knowledge required by teachers for technology integration in their teaching, while

addressing the complex, multifaceted and situated nature of teacher knowledge” (Mishra &

Koehler, 2006):

Figure 1. Technological Pedagogical Content Knowledge Framework (TPACK)

Source: Mishra & Koehler, 2006.

Technology integration is a complex and “wicked” problem (Mishra & Koehler, 2006)

that the educational technology field has long struggled to understand, define, and explain.

The TPCK framework offers us a possible solution.

Aim of the Study

The purpose of this study was to investigate pre-service science teachers’ self-

confidence level about technological pedagogical content knowledge with the Technological

Pedagogical Content Knowledge Confidence Survey (TPCKCS) translated by Timur and

Taşar (2011).

Significance of the Study

Use of technology in education has become more and more important as the time

goes on. The inclusion of technology in the learning process causes changes in the teaching

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methods currently used in learning environments. It is also important that the teacher

candidates are dominated by these changes and that they are cultivated in this direction.

Teacher education programs should invest in their teachers by thinking long term while

preparing their graduates for their professional lives. It is an inevitable reality for today to

integrate technology with the learning process. Successful candidates who fulfill this task in

the schools they will be working with in the future are linked to the experiences they have

received in the pre-service period and their experiences of learning realistically. In the

teacher education program, technology knowledge must be acquired in the context of the

roles that teachers must assume when technology is integrated into the learning process.

Because the teachers will apply technology to make the learning process effective and

efficient.

Some studies indicated that teachers are not clear about how to use technology to

assist their teaching. Sometimes they use the Web to attract students’ attention but they do

not know how to use it to facilitate students’ development (Lee & Tsai, 2008). Teachers’ level

of TPACK is the determinant that they can successfully integrate technology into education.

Besides, their confidence on integrating technology in education (self-confidence) and their

motivation while they are teaching (outcome expectations) are critical (Niederhauser &

Perkmen, 2010).

According to Niess (2008), pre-service teachers needed to learn and develop their

TPACK and also, she called attention to methods courses in a such a way that preservice

teachers should learn how to teach TPACK ways of thinking before they are ready to teach

through methods courses since she claimed that methods courses provide practical

experiences to preservice teachers (pp. 227-228).

There is lack of research on teachers’ TPACK especially in Turkey, related to self-

confidence expectation levels in science education settings. Role of science education teacher

education programs on development of these variables are not clear, either. Therefore,

studying these technology-related perceptions of the pre-service science education teachers

can improve our understanding of the strengths and weaknesses of those programs in

preparation of future teachers who are expected to educate digital natives.

Research Questions

This study focused on the following two research questions:

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1) What is the perceived confidence level of pre-service science teachers related to the four

Technological Pedagogical Content Knowledge (TPACK) constructs (i.e., Technological

Knowledge (TK), Technological Pedagogical Knowledge (TPK), Technological Content

Knowledge (TCK) and Technological Pedagogical Content Knowledge (TPCK))?

2) What are differences pre-service science teachers’ who they attend in different grade level,

self-confidence levels?

Method

Research Design

In order to determine pre-service science teachers' self-confidence of TPACK both

quantitative and qualitative research methods were used in this study. This research is a

multiple case study based on a mixed methods research design. The quantitative data was

collected by “TPACK in Science Survey (TPACKSS)” developed by Graham, Burgoyne,

Cantrell, Smith, Clair and Harris (2009). The survey adapted to Turkish and its Cronbach’s

alpha was calculated .95. The instrument was applied 123 pre-service science teachers by the

researcher. The quantitative data were analyzed using the Statistical Package for the Social

Sciences (SPSS). Qualitative data was collected via semi-structured interviews. These

interviews with the pre-service science teachers were recorded in audio and transcribed

verbatim. The aim of the interviews was to collect more detailed information from the

participants. According to Creswell (1998), qualitative research must show enough detail for

the reader to be able to see the case clearly in order for the researcher’s conclusion to make

sense.

Participants and Sampling Procedure

This study was conducted with preservice science teachers who are enrolled in

elementary science education departments of Education Faculties of one public university

located in Central Anatolia. The accessible population constitutes the pre-service science

teachers enrolled in this university. At her convenience the researcher was able to collect

data from one public university. A non-random purposeful sample was used to gather data

from pre-service science teachers. 123 pre-service science teachers participated in the study

on a voluntary basis. Participants’ characteristics are summarised in Table 1.

Table 1. Participants’ Characteristics

Gender N %

Female 102 82.93

Male 21 17.07

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General characteristics of the participants were provided in Table 1. According to

Table 2.1 most of the participants were female (82.9 %). The other participants were male

(17.07 %).

Instruments

The Technological Pedagogical Content Knowledge Confidence Science (TPCKCS)

instrument which translated to Turkish by Timur and Taşar (2011) was used in this study.

The original survey instrument was created by Graham et al. (2009) and consists of 31 Likert-

type items. Respondents were asked: “How confident are you in your current ability to

complete each of the following tasks?” Responses were given in the form of 6-point Likert-

type questions: 1=not confident at all, 2=slightly confident, 3=somewhat confident, 4=fairly

confident, 5=quite confident, 6=completely confident (the scale for TCK items also had 0=I

don’t know about this kind of technology). The areas of Technological Pedagogical Content

Knowledge (TPCK), Technological Pedagogical Knowledge (TPK), Technological Content

Knowledge (TCK) and Technological Content (TK) were created by combining the domains

of content, pedagogy and technology. The original instrument contains eight items related to

TPCK, seven items related to TPK, five items related to TCK, and 11 items related to TK in

order to measure in-service science teachers’ TPCK confidence (Timur & Taşar, 2011).

After translating the instrument into Turkish, a back translation into English was

made for checking purposes. First, three native Turkish speakers made their translations

independently. Second, three back translations into English were made by three

independent Turkish individuals with PhD degrees. To determine the instruments’ validity

and reliability, a revised version of the scale was applied to 393 science and technology

teachers. Confirmatory factor analysis was conducted to ensure compliance with Turkish

culture. The instrument consisted of 31 items and four dimensions: Technological

Pedagogical Content Knowledge (TPCK), Technological Pedagogical Knowledge (TPK),

Technological Content Knowledge (TCK) and Technological Knowledge (TK). Reliability

analysis of the instrument revealed that the Cronbach-Alpha coefficient was very high (.92)

for the whole instrument. Moreover, the reliability coefficients of the four sub-dimensions

were also very high, at .89, .87, .89 and .86 respectively for the TPCK, TPK, TCK, and TK sub-

dimensions (Timur & Taşar, 2011).

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Additionally, open-ended questions were asked by the researcher. Open-ended

questions were answered by 10 males and 10 female pre-service science teachers.

Data Collection Procedures

The survey instruments were administered to the preservice science teachers in their

classrooms. The approximate time of filling the scale was 10-15 minutes. The researcher

administered the questionnaire to the participants. Before administration, the researcher

informed the participants about how to fill the questionnaire. Moreover, the researcher

stayed in the class to answer the further questions coming from the participants. Throughout

this procedure, the researcher tried to ensure the consistency in data collection procedure.

Data Analysis Procedures

Data which were gathered from the preservice teachers were analyzed to the SPSS 18,

Statistical Package for the Social Sciences. The imported data were analyzed by using

descriptive statistics. Descriptive statistics was used to summarize, organize and simplify

data. The mean of each of the component of TPACK was able to be calculated. The standard

deviation of each of the component of TPACK was able to be calculated. The Cronbach’s

alpha of each of the component of TPACK was able to be calculated.

Multivariate Analysis of Variance (MANOVA) was conducted to investigate the

impact of different grade level on perceived technological pedagogical content knowledge

(TPCK) on preservice science teachers.

For analysis of qualitative data, it was conducted the content analysis as the data was

coded, themes were found, the data was organized and defined according to the codes and

themes, and interpretations were made.

Findings

In order to address the first research question of the perceived confidence level of

preservice science teachers related to the four TPACK constructs, preservice teachers were

asked, “How would you rate your confidence in doing the following tasks associated with

technology usage?” Thirty-one items in the areas of technological knowledge (TK),

technological pedagogical knowledge (TPK), technological content knowledge (TCK), and

technological pedagogical content knowledge (TPCK) were asked, and responses were made

on a 5-point scale reflecting the level of confidence. Mean, standard deviation and

Cronbach’s alpha were calculated for the four sub-dimensions is Table 3, while Table 3

shows the ranges of confidence levels formed.

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Table 2. The Confidence Interval for the Likert Scale

Table 3. Summary of Descriptive Statistics for Sub-dimensions

According to Table 3, the pre-service science teachers asserted that they feel fairly

confident in technological pedagogical content knowledge (TPCKmean=3.69) technological

pedagogical knowledge (TPKmean=3.81) and technological knowledge (TKmean=3.65). Hovewer,

they feel somewhat confident in technological content knowledge (TCKmean=3.16). Reliability

analysis of the instrument revealed that the Cronbach-Alpha coefficient was very high (.93)

for the whole instrument. The reliability coefficients (α) of the four sub-dimensions were also

very high, at .82, .87, .86 and .88 respectively for the TPCK, TPK, TCK, and TK sub-

dimensions.

Standard deviation is the most common measure of statistical dispersion, measuring

how widely spread the values in a data set are. If the data points are all close to the mean,

then the standard deviation is close to zero. If many data points are far from the mean, then

the standard deviation is far from zero. If all the data values are equal, then the standard

deviation is zero. A set with a low standard deviation has most of the data points centered

around the average. A set with a high standard deviation has data points that are not so

clustered around the average. The standard deviation of the four sub-dimensions were

different from each other, at .67, .69, 1.19 and .79 respectively for the TPCK, TPK, TCK, and

TK sub-dimensions.

Interval range Confidence level

1.00-1.79 Not confident at all

1.80-2.59 Slightly confident

2.60-3.39 Somewhat confident

3.40-4.19 Fairly confident

4.20-5.00 Completely confident

Sub-dimension Number of Items M SD α

TPCK 8 3.69 .67 .82

TPK 7 3.81 .69 .87

TCK 5 3.16 1.19 .86

TK 11 3.65 .79 .88

Total .93

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Table 4. Descriptive Statistic Results of Frequency

From this frequency Table 4, we have quickly concluded that in item 1, many of the

participants selected 50% trust. In item 2, many of the participants selected 50% trust. In item

3, many of the participants selected mostly trust. In item 4, many of the participants selected

mostly trust. In item 5, many of the participants selected 50% trust and mostly trust equally.

In item 6, many of the participants selected mostly trust. In item 7, many of the participants

selected mostly trust. In item 8, many of the participants selected 50% trust. In item 9, many

of the participants selected mostly trust.

In item 10, many of the participants selected mostly trust. In item 11, many of the

participants selected mostly trust. In item 12, many of the participants selected mostly trust.

Items None

trust

Rarely

trust

%50 trust Mostly

trust

Totally trust I do

not

know

S1 1 18 54 23 27

S2 3 13 43 35 29

S3 1 8 35 49 30

S4 2 7 29 49 36

S5 2 9 35 35 22

S6 2 11 34 51 25

S7 3 15 29 50 26

S8 1 10 46 45 21

S9 2 5 21 59 36

S10 3 12 27 47 34

S11 3 7 31 46 36

S12 2 10 30 48 33

S13 1 5 26 52 39

S14 2 8 30 54 29

S15 4 11 40 41 27

S16 3 10 34 40 22 14

S17 5 11 33 39 18 17

S18 6 21 23 41 20 12

S19 5 19 28 35 26 10

S20 4 16 30 33 29 11

S21 1 19 24 34 45

S22 1 6 24 32 60

S23 2 17 30 29 45

S24 3 10 25 36 49

S25 2 11 34 33 43

S26 7 19 36 32 29

S27 12 16 38 26 31

S28 2 11 24 38 47

S29 5 12 34 36 36

S30 26 20 28 25 24

S31 21 26 31 17 28

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In item 13, many of the participants selected mostly trust. In item 14, many of the

participants selected mostly trust. In item 15, many of the participants selected mostly trust.

In item 16, many of the participants selected mostly trust. In item 17, many of the

participants selected mostly trust. In item 18, many of the participants selected mostly trust.

In item 19, many of the participants selected mostly trust. In item 20, many of the

participants selected mostly trust. In item 21, many of the participants selected totally trust.

In item 22, many of the participants selected totally trust.

In item 23, many of the participants selected totally trust. In item 24, many of the

participants selected totally trust. In item 25, many of the participants selected totally trust.

In item 26, many of the participants selected 50% trust. In item 27, many of the participants

selected 50% trust. In item 28, many of the participants selected totally trust. In item 29,

many of the participants selected mostly trust and totally trust equally. In item 30, many of

the participants selected 50% trust. In item 31, many of the participants selected 50% trust.

According to Table 4, many of the participants feel mostly trust in the technological

pedagogical knowledge (TPK) and the technological content knowledge (TCK) sub-

dimensions. Moreover, in the technological knowledge (TK) sub-dimension, many of the

participants feel totally trust.

In order to address the second research question, there were 4 dependent variables

(TPCK, TPK, TCK and TK) of interest and one independent variable (grade level) with;

therefore, multivariate analysis of variance (MANOVA) was conducted to investigate mean

differences among them. Preliminary assumption testing was conducted to check for

normality, linearity, univariate and multivariate outliers, homogeneity of variance-

covariance matrices, and multicollinearity.

1. Sample size. There were more cases in each cell than the number of dependent

variable. There were 4 dependent variables and the sample was 123. Therefore, this

assumption was not violated.

2. Normality. Univariate and multivariate normalities were checked. Univariate

normality was checked by examining skewness, kurtosis values and by visual examination

of histograms. The skewness and kurtosis values were in acceptable range which is between

-2 and +2 for all dependent variables. In order to check multivariate normality, Mahalanobis

distances were calculated to compare the critical value given in the Chi-square table (Pallant,

2007).

135

3. Outliers. In order to determine outliers Mahalanobis distances were examined.

Moreover, the outliers can be accepted since there was a reasonable size data file (Pallant,

2007).

4. Linearity. In order to check linearity scatterplots were generated for each

dependent variable pair. The scatterplots revealed that there was no apparent violation of

linearity assumption.

5. Multicollinearity and singularity. These assumptions were checked by calculating

the correlation coefficients between dependent variables. There was no violation of this

assumption.

6. Homogeneity of variances. In order to check this assumption, Levene’s Test of

Equality of Error Variances was checked. According to the Table 3.4, the error variance of the

dependent variable was not equal across groups for all DVs. This assumption was not

assured for TPCK (p = .803), TPK (p = .793), TCK (p = .055), and TK (p= .058).

Table 5. Levene’s Test Equality of Variances

F df1 df2 Sig.

TPCK ,331 3 119 ,803

TPK ,345 3 119 ,793

TCK 2,605 3 119 ,055

TK 2,563 3 119 ,058

After checking the assumptions of MANOVA, analysis was conducted.

Table 6. Multivariate Test Result

Effect Value F Hypothesis

df

Error df Sig. Partial Eta

Squared

Sinif Pillai's

Trace

,198 2,086 12,000 354,000 ,017 ,066

Wilks'

Lambda

,807 2,162 12,000 307,199 ,013 ,069

Hoteling’s

Trace

,233 2,226 12,000 344,000 ,010 ,072

Roy's

Largest

Root

,203 5,982b 4,000 118,000 ,000 ,169

Table 6 revealed that there was a statistically significant mean difference for grade

level on the combined dependent variables, F (4,118) = 5,982 p = .000; Roy’s Largest Root =

.203; partial eta squared = .169 indicating small effect size.

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Table 7. Descriptive Statistics Results of MANOVA

Sınıf Mean Std. Deviation N

TPCK 1 28,73 5,63482 41

2 28,7353 5,49550 34

3 30,5385 5,27053 26

4 30,50 4,41588 22

Total 29,4309 5,32867 123

TPK 1 26,3659 5,43487 41

2 26,3824 5,105 34

3 27,6154 4,622 26

4 27,7273 4,56886 22

Total 26,8780 5,01078 123

TCK 1 12,7561 6,56422 41

2 16,2647 4,64699 34

3 19,0769 4,59498 26

4 17,0909 5,79745 22

Total 15,8374 5,98684 123

TK 1 39,5610 7,88685 41

2 38,4412 9,36205 34

3 42,6538 7,33181 26

4 40,9091 10,12262 22

Total 40,1463 8,66752 123

Table 7 shows that there are differences among means and standard deviations for

four sub-dimensions. Moreover; Table revealed that, if the grade level increases mean scores

will increase for their sub- dimensions which are TPCK, TPK and TK. In the technological

content knowledges (TCK) sub- dimension, grade level increase, but mean scores do not

increase constantly.

In the technological pedagogical content knowledge sub- dimension, mean score is

28,73 for grade level 1. Mean score is 28,7353 for grade level 2. Mean score is 30,5385 for

grade level 3. Mean score is 30,50 for grade level 4.

In technological pedagogical knowledge sub- dimension, mean score is 26,3659 for

grade level 1. Mean score is 26,3824 for grade level 2. Mean score is 27,6154 for grade level 3.

Mean score is 27,7273 for grade level 4.

In the technological content knowledge sub- dimension, mean score is 12,7561 for

grade level 1. Mean score is 16,2647 for grade level 2. Mean score is 19,0769 for grade level 3.

Mean score is 17, 0909 for grade level 4.

In technological knowledge sub- dimension, mean score is 39,5610 for grade level 1.

Mean score is 38,4412 for grade level 2. Mean score is 42,6538 for grade level 3. Mean score is

42,9091 for grade level 4.

137

According to Table 7, the technological knowledge (TK) sub- dimension has the

highest mean score in the total. The technological content knowledge (TCK) sub- dimension

has the lowest mean score in the total.

In order to investigate whether grade level differed in all dependent variables or not,

between- subjects’ effects were examined. Table 7 shows that between- subjects’ effects were

examined to better understand the difference in relation to each of dependent variables.

Table 8. Test of Between-Subjects Effect

Source Dependent

Variables

Type III

Sum of

Squares

Mean

Square

F Sig. Partial Eta

Squarel

Sınıf TPCK 93,535 31,178 1,101 .352 .027

TPK 49,112 16,371 ,646 .587 .016

TCK 702,905 234,302 7,598 .000 .161

TK 289,183 96,394 1,292 .280 .032

In order to identify where the significant differences lie, post-hoc analyses were

conducted for TPCK, TPK, TCK, and TK.

The Table 8 shows that, each comparison was tested with Bonferroni. The results of

the analysis revealed that there was a statistically significant difference in TCK means score

at the p <.05 level for different grade level. Although there is statistically significant

difference, the actual difference in mean scores between the groups. According to Table 8,

there are significant differences between first grade level and third grade level. These

differences are in favor of third class level. There are significant differences between first

class level and fourth class level. These differences are in favor of fourth class level. We have

concluded that if the grade level increases, self-confidence of participants will increase.

The open-ended questions show that pre-service science teachers have sufficient

TPACK confidence in science teaching, and that they know the importance of the using

technology in science teaching. Koch (2005) explains, a computer can become part of the

science learning experience if the child feels a need to use it in learning.

Pre-service science teachers asserted that they use computers for showing animations,

simulations, videos and films, and for making representations with Power- Point during

instruction. Pre-service science teachers tend to group the whole class for showing

animations, simulations and videos using a projector.

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Qualitative analysis shows that there is a need to provide TPACK confidence to pre-

service science teachers in order to create technology-enhanced classrooms. It is important to

improve the education.

As a consequence, in qualitative analysis, this analysis indicates that it is possible to

design suitable technology rich environments to address, and develop, students

understanding of the knowledge components suggested by the TPACK framework.

Discussion and Conclusion

This study aimed to gather information about the self- confidence level of pre- service

science teacher on TPACK. The findings of both descriptive analysis and content analysis can

be used as foreknowledge in further research. These results indicate that the Technological

Pedagogical Content Knowledge Confidence Science (TPCKCS) instrument is a promising

instrument for measuring pre-service teachers’ self-confidence level of the TPACK.

Moreover, the relationships between the content knowledge and the components of

the TPACK and among the components of the TPACK were investigated to understand the

complex structure of the framework and to make clear the interdependence of the

knowledge in the framework. The study shows that pre-service science teachers have

sufficient related to the four Technological Pedagogical Content Knowledge (TPACK)

constructs.

According to Turkish Education Association (2009) report, pre-service teachers need

to have technology competences, or so-called technological pedagogical content knowledge.

They have to know how to integrate technology into their instruction and create effective

technology-rich environments. Moreover; there is a need to provide TPACK confidence to

pre-service science teachers in order to create optimally functioning technology-enhanced

classrooms. It is important to devote time and effort to PD programmes, to exploring the

cognitive, transformative and pedagogical aspects of adopting educational technology in

teaching, rather than merely presenting the hardware and software to be used (Sturdivant,

Dunham, & Jardine, 2009).

There are some recommendations for future research. First of all, this study was

limited to the pre-service science teachers. More studies can be done with in-service teacher,

mathematics teachers and technology teachers. Technological pedagogical content

knowledge, technology integration self-efficacy and instructional technology outcome

expectations of pre-service science education teachers should be examined.

139

Acknowledgements: This study was supported by Turkish Scientific and Technical Research

Council (TUBITAK)

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